Context rot degrades AI agents as conversations grow longer

AI agents suffer from context rot, where performance degrades as conversation history accumulates. Recency bias, instruction dilution, stale state pollution, and token budget pressure cause the model to forget earlier instructions and produce worse outputs. Developers can detect it by testing instruction compliance at different conversation lengths.